A Bayesian Framework for Model Uncertainty Considerations in Fire Simulation Codes

Over the past twenty years, computer-based simulation codes have emerged as the leading tools to assess risks of severe events such as fire. The results of such simulation codes are usually used to estimate the magnitude, frequency and consequence of hazards. A typical simulation program/model utilizes many different sub-models, each characterizing a physical or chemical process contributing to exposure of the hazard or occurrence of certain adverse failure events. The final prediction made by such simulation codes can be temporal, spatial or just a single estimate for the measure of interest. The predictions made by the simulation codes are subject to different contributing uncertainties, including the uncertainty about the inputs to the code, uncertainty of sub-models used in the codes and uncertainty in the parameters of the probabilistic models (if applicable) used in the codes to characterize (e.g., validate) code outputs. A primary way to measure the model uncertainties is to perform independent experiments and assess conformance of the models to observations from such experiments. It is very important to note that the experimental results themselves may also involve uncertainties, for example due to measurement errors and lack of precision in instrumentation. In this research experimental data collected as part of the Fire Model Verification and Validation [1] are used to characterize the share of model uncertainty in the total code output uncertainty, when experimental data are compared to the code predictions. In this particular case, one should assume the uncertainty of experiments (e.g., due to sensor or material variability) is available from independent sources. The outcome of this study is the probabilistic estimation of uncertainty associated with the model and the corresponding uncertainty in the predictions made by the simulation code. A Bayesian framework was developed in this research to assess fire model prediction uncertainties in light of uncertain experimental observations. In this research the complexity of the Bayesian inference equations was overcome by adopting a Markov Chain Monte Carlo (MCMC) simulation technique. This paper will discuss the Bayesian framework, examples of using this framework in assessing fire model uncertainties, and a discussion of how the results can be used in risk-informed analyses.